





'''simple docstring'''






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class 		lowerCamelCase	(   _A   , _A							):



	@register_to_config
	def __init__( self		,   a_ = 128		,   a_ = 256		,   a_ = 2000.0		,   a_ = 768		,   a_ = 12		,   a_ = 12		,   a_ = 64		,   a_ = 2_048		,   a_ = 0.1		,   ):
			super().__init__()

			lowerCAmelCase  :     Any       						= nn.Sequential(
			    nn.Linear(a_		,   d_model * 4		,   bias=a_						)		,   nn.SiLU()		,   nn.Linear(d_model * 4		,   d_model * 4		,   bias=a_						)		,   nn.SiLU()		,   )

			lowerCAmelCase  :     int       						= nn.Embedding(a_		,   a_						)
			lowerCAmelCase  :     Any       						= False

			lowerCAmelCase  :     List[str]       						= nn.Linear(a_		,   a_		,   bias=a_						)

			lowerCAmelCase  :     Union[str, Any]       						= nn.Dropout(p=a_						)

			lowerCAmelCase  :     List[str]       						= nn.ModuleList()
			for lyr_num in range(a_						):
					# FiLM conditional T5 decoder
					lowerCAmelCase  :     List[str]       						= DecoderLayer(d_model=a_		,   d_kv=a_		,   num_heads=a_		,   d_ff=a_		,   dropout_rate=a_						)
					self.decoders.append(a_						)

			lowerCAmelCase  :     List[str]       						= TaLayerNorm(a_						)

			lowerCAmelCase  :     Any       						= nn.Dropout(p=a_						)
			lowerCAmelCase  :     List[Any]       						= nn.Linear(a_		,   a_		,   bias=a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			lowerCAmelCase  :     str       						= torch.mul(query_input.unsqueeze(-1						)		,   key_input.unsqueeze(-2						)						)
			return mask.unsqueeze(-3						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= decoder_input_tokens.shape
			assert decoder_noise_time.shape == (batch,)

			# decoder_noise_time is in [0, 1), so rescale to expected timing range.
			lowerCAmelCase  :     Tuple       						= get_timestep_embedding(
			    decoder_noise_time * self.config.max_decoder_noise_time		,   embedding_dim=self.config.d_model		,   max_period=self.config.max_decoder_noise_time		,   ).to(dtype=self.dtype						)

			lowerCAmelCase  :     List[str]       						= self.conditioning_emb(a_						).unsqueeze(1						)

			assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

			lowerCAmelCase  :     Optional[int]       						= decoder_input_tokens.shape[1]

			# If we want to use relative positions for audio context, we can just offset
			# this sequence by the length of encodings_and_masks.
			lowerCAmelCase  :     Optional[int]       						= torch.broadcast_to(
			    torch.arange(a_		,   device=decoder_input_tokens.device						)		,   (batch, seq_length)		,   )

			lowerCAmelCase  :     Optional[int]       						= self.position_encoding(a_						)

			lowerCAmelCase  :     Optional[Any]       						= self.continuous_inputs_projection(a_						)
			inputs += position_encodings
			lowerCAmelCase  :     Optional[Any]       						= self.dropout(a_						)

			# decoder: No padding present.
			lowerCAmelCase  :     str       						= torch.ones(
			    decoder_input_tokens.shape[:2]		,   device=decoder_input_tokens.device		,   dtype=inputs.dtype						)

			# Translate encoding masks to encoder-decoder masks.
			lowerCAmelCase  :     Dict       						= [(x, self.encoder_decoder_mask(a_		,   a_						)) for x, y in encodings_and_masks]

			# cross attend style: concat encodings
			lowerCAmelCase  :     List[str]       						= torch.cat([x[0] for x in encodings_and_encdec_masks]		,   dim=1						)
			lowerCAmelCase  :     Optional[int]       						= torch.cat([x[1] for x in encodings_and_encdec_masks]		,   dim=-1						)

			for lyr in self.decoders:
					lowerCAmelCase  :     Tuple       						= lyr(
					    a_		,   conditioning_emb=a_		,   encoder_hidden_states=a_		,   encoder_attention_mask=a_		,   )[0]

			lowerCAmelCase  :     Tuple       						= self.decoder_norm(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= self.post_dropout(a_						)

			lowerCAmelCase  :     List[Any]       						= self.spec_out(a_						)
			return spec_out






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_=1e-6						):
			super().__init__()
			lowerCAmelCase  :     List[str]       						= nn.ModuleList()

			# cond self attention: layer 0
			self.layer.append(
			    TaLayerSelfAttentionCond(d_model=a_		,   d_kv=a_		,   num_heads=a_		,   dropout_rate=a_						)						)

			# cross attention: layer 1
			self.layer.append(
			    TaLayerCrossAttention(
			        d_model=a_		,   d_kv=a_		,   num_heads=a_		,   dropout_rate=a_		,   layer_norm_epsilon=a_		,   )						)

			# Film Cond MLP + dropout: last layer
			self.layer.append(
			    TaLayerFFCond(d_model=a_		,   d_ff=a_		,   dropout_rate=a_		,   layer_norm_epsilon=a_						)						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_=None		,   a_=None		,   a_=None		,   a_=None		,   a_=None		,   ):
			lowerCAmelCase  :     int       						= self.layer[0](
			    a_		,   conditioning_emb=a_		,   attention_mask=a_		,   )

			if encoder_hidden_states is not None:
					lowerCAmelCase  :     Union[str, Any]       						= torch.where(encoder_attention_mask > 0		,   0		,   -1e1_0						).to(
					    encoder_hidden_states.dtype						)

					lowerCAmelCase  :     int       						= self.layer[1](
					    a_		,   key_value_states=a_		,   attention_mask=a_		,   )

			# Apply Film Conditional Feed Forward layer
			lowerCAmelCase  :     List[str]       						= self.layer[-1](a_		,   a_						)

			return (hidden_states,)






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_						):
			super().__init__()
			lowerCAmelCase  :     List[Any]       						= TaLayerNorm(a_						)
			lowerCAmelCase  :     List[Any]       						= TaFiLMLayer(in_features=d_model * 4		,   out_features=a_						)
			lowerCAmelCase  :     Optional[Any]       						= Attention(query_dim=a_		,   heads=a_		,   dim_head=a_		,   out_bias=a_		,   scale_qk=a_						)
			lowerCAmelCase  :     List[str]       						= nn.Dropout(a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_=None		,   a_=None		,   ):
			# pre_self_attention_layer_norm
			lowerCAmelCase  :     int       						= self.layer_norm(a_						)

			if conditioning_emb is not None:
					lowerCAmelCase  :     Optional[Any]       						= self.FiLMLayer(a_		,   a_						)

			# Self-attention block
			lowerCAmelCase  :     List[str]       						= self.attention(a_						)

			lowerCAmelCase  :     List[Any]       						= hidden_states + self.dropout(a_						)

			return hidden_states






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_						):
			super().__init__()
			lowerCAmelCase  :     str       						= Attention(query_dim=a_		,   heads=a_		,   dim_head=a_		,   out_bias=a_		,   scale_qk=a_						)
			lowerCAmelCase  :     Optional[Any]       						= TaLayerNorm(a_		,   eps=a_						)
			lowerCAmelCase  :     List[Any]       						= nn.Dropout(a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_=None		,   a_=None		,   ):
			lowerCAmelCase  :     Dict       						= self.layer_norm(a_						)
			lowerCAmelCase  :     int       						= self.attention(
			    a_		,   encoder_hidden_states=a_		,   attention_mask=attention_mask.squeeze(1						)		,   )
			lowerCAmelCase  :     int       						= hidden_states + self.dropout(a_						)
			return layer_output






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_						):
			super().__init__()
			lowerCAmelCase  :     List[str]       						= TaDenseGatedActDense(d_model=a_		,   d_ff=a_		,   dropout_rate=a_						)
			lowerCAmelCase  :     Union[str, Any]       						= TaFiLMLayer(in_features=d_model * 4		,   out_features=a_						)
			lowerCAmelCase  :     List[Any]       						= TaLayerNorm(a_		,   eps=a_						)
			lowerCAmelCase  :     Tuple       						= nn.Dropout(a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_=None						):
			lowerCAmelCase  :     List[Any]       						= self.layer_norm(a_						)
			if conditioning_emb is not None:
					lowerCAmelCase  :     Dict       						= self.film(a_		,   a_						)

			lowerCAmelCase  :     int       						= self.DenseReluDense(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= hidden_states + self.dropout(a_						)
			return hidden_states






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_		,   a_		,   a_						):
			super().__init__()
			lowerCAmelCase  :     Union[str, Any]       						= nn.Linear(a_		,   a_		,   bias=a_						)
			lowerCAmelCase  :     Optional[Any]       						= nn.Linear(a_		,   a_		,   bias=a_						)
			lowerCAmelCase  :     Dict       						= nn.Linear(a_		,   a_		,   bias=a_						)
			lowerCAmelCase  :     Tuple       						= nn.Dropout(a_						)
			lowerCAmelCase  :     str       						= NewGELUActivation()





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     int       						= self.act(self.wi_a(a_						)						)
			lowerCAmelCase  :     Optional[int]       						= self.wi_a(a_						)
			lowerCAmelCase  :     Optional[Any]       						= hidden_gelu * hidden_linear
			lowerCAmelCase  :     Dict       						= self.dropout(a_						)

			lowerCAmelCase  :     str       						= self.wo(a_						)
			return hidden_states






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_		,   a_=1e-6						):
			super().__init__()
			lowerCAmelCase  :     str       						= nn.Parameter(torch.ones(a_						)						)
			lowerCAmelCase  :     Optional[int]       						= eps





	def 	_lowerCamelCase     ( self		,   a_						):
			# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
			# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
			# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
			# half-precision inputs is done in fp32

			lowerCAmelCase  :     List[Any]       						= hidden_states.to(torch.floataa						).pow(2						).mean(-1		,   keepdim=a_						)
			lowerCAmelCase  :     Any       						= hidden_states * torch.rsqrt(variance + self.variance_epsilon						)

			# convert into half-precision if necessary
			if self.weight.dtype in [torch.floataa, torch.bfloataa]:
					lowerCAmelCase  :     int       						= hidden_states.to(self.weight.dtype						)

			return self.weight * hidden_states






class 		lowerCamelCase	(   nn.Module							):



	def 	_lowerCamelCase     ( self		,   a_						):
			return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.044715 * torch.pow(a_		,   3.0						))						))






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_		,   a_						):
			super().__init__()
			lowerCAmelCase  :     Optional[Any]       						= nn.Linear(a_		,   out_features * 2		,   bias=a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			lowerCAmelCase  :     str       						= self.scale_bias(a_						)
			lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= torch.chunk(a_		,   2		,   -1						)
			lowerCAmelCase  :     Optional[int]       						= x * (1 + scale) + shift
			return x










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase									=   {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



